Optimal Multiscale Learning of Linear Operators
Researchers have established the statistical and computational limits for learning bounded linear operators between Sobolev spaces using noisy input-output data. The problem is reframed as an infinite-dimensional matrix regression with a complex multiscale structure. A novel blockwise least-squares estimator has been developed that achieves optimal rates and computational efficiency by adapting sample sizes to different scales. AI